COVID-19 detection using machine learning and fusion-based deep learning models


  • Fatima Raheem -
  • Manaf K. Hussein n



Machine Learning, Deep Learning, SVM, COVID-19, Model Fusion


The COVID-19 pandemic has been one of the most challenging crises attacking the world in the last three years. Many systems have been introduced in the field of COVID-19 detection.

In this research, machine learning and deep learning models for the detection of COVID-19 with a probability of the presence of COVID-19 are proposed. In the machine learning scenario, the COVID-19 dataset is split into 70% training and 30% testing, and a segmentation process is applied to the CT images in order to get the lung ROI only. The features of CT images are then extracted using Gabor-Wavelet and deep-based features. The SVM classifier is then trained and evaluated. For the deep learning model, the CT images are fed into the model without feature extraction, and three different DL models (CNN, GoogleNet, and ResNet50) are trained and evaluated. Other scenarios are proposed in which the SVM Gabor-Wavelet and deep features are fused, and the three deep learning models are also fused to get better performance. The experiments show that the best model is the deep-based fusion model by which the system achieved 96.4156%, 96.1905%, and 96.1905% for accuracy, precision, and recall, respectively.


Benmalek, E., Elmhamdi, J., & Jilbab, A. (2021). Comparing CT scan and chest X-ray imaging for COVID-19 diagnosis. Biomedical Engineering Advances, 1, 100003.

Li, Y., Yao, L., Li, J., Chen, L., Song, Y., Cai, Z., & Yang, C. (2020). Stability issues of RT‐PCR testing of SARS‐CoV‐2 for hospitalized patients clinically diagnosed with COVID‐19. Journal of medical virology, 92(7):903-908.

Grewal, M., Srivastava, M. M., Kumar, P., & Varadarajan, S. (2018). Radnet: Radiologist-level accuracy using deep learning for hemorrhage detection in ct scans. In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 281-284.

Ucar, F., & Korkmaz, D. (2020). COVIDiagnosis-Net: Deep Bayes-SqueezeNet-based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical hypotheses, 140, 109761.

Ozturk T., Talo M., Yildirim E. A., Baloglu U. B., Yildirim O. and Acharya U. R. (2020). "Automated detection of COVID-19 cases using deep neural networks with X-ray images", Comput. Biol. Med., 121.

Wang, L., Lin, Z. Q., & Wong, A. (2020). Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest x-ray images. Scientific Reports, 10(1): 1-12.

Mayya, A., & Khozama, S. (2020). A Novel Medical Support Deep Learning Fusion Model for the Diagnosis of COVID-19. In 2020 IEEE International Conference on Advent Trends in Multidisciplinary Research and Innovation (ICATMRI), 1-6.

Kedia, P., & Katarya, R. (2021). Covent-19: A Deep Learning model for the detection and analysis of COVID-19 patients. Applied Soft Computing, 104, 107184.

Haghanifar, A., Majdabadi, M. M., Choi, Y., Deivalakshmi, S., & Ko, S. (2022). Covid-connect: Detecting covid-19 in frontal chest x-ray images using deep learning. Multimedia Tools and Applications, 1-31.

Ye Z., Zhang Y., Wang Y., Huang Z., and Song B., (2020). "Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review," European Radiology, 30(8):4381-4389.

Bernheim A., Mei X., Huang M., Yang Y., Fayad Z. A., Zhang N. and Diao K., (2020). "Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection," radiology.

Hamdan H. Z., Elgaili Y. O. and Dosogi W. A. A., (2020). "Natural resistance-associated macrophage protein-1 gene polymorphisms and genetic susceptibility to pulmonary tuberculosis in Sudanese patients," Buletinul Academiei de Ştiinţe a Moldovei. Ştiinţe Medicale, 69(1):63-72.

Wu J. X., Guo D., Fang Z., Chen L., Huang H. and Li C., (2020). "Chest CT findings in patients with coronavirus disease 2019 and its relationship with clinical features," Investigative Radiology, 55(5).

Gillespie M., Flannery P., Schumann J. A., Dincher N., Mills R., and Can A., (2020). "Crazy-paving: a computed tomographic finding of coronavirus disease 2019," Clinical Practice and Cases in Emergency Medicine, 4(3).

Shi H., Han X., Jiang N., Cao Y., Alwalid O., Gu J., Fan Y., and Zheng C., (2020). "Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study," The Lancet infectious diseases, 20(4):245-434.

K. Li, J. Wu, F. Wu, D. Guo, L. Chen, Z. Fang, and C. Li, "The clinical and chest CT features associated with severe and critical COVID-19 pneumonia," Investigative Radiology, 2020.

Cai Y., Liu J., Yang H., Chen T., Yu Q., Chen J. and Huang D., (2021). "Correlation between early features and prognosis of symptomatic COVID-19 discharged patients in Hunan, China," Scientific Reports, 11(1):1-9.

Han R., Huang L., Jiang H., Dong J., Peng H. and Zhang D., (2020). "Early Clinical and CT Manifestations of Coronavirus Disease 2019 (COVID-19) Pneumonia," American Journal of Roentgenology, 215(2):338-343.

Rasuli B., "Crazy paving pattern in COVID-19 pneumonia," radio media, 2020. [Online]. Available: [Accessed 10 8 2022].

Kurani A., Doshi P., Vakharia A. and Shah M., (2021). "A comprehensive comparative study of artificial neural network (ANN) and support vector machines (SVM) on stock forecasting," Annals of Data Science, 1-26.

Alyasseri Z. A. A., Al‐Betar M. A., Doush I. A., Awadallah M. A., Abasi A. K., Makhadmeh S. N., and Alomari O. A., "Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches," Expert systems, 39(3).

Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V. and Rabinovich A., (2015). "Going Deeper with Convolutions," In Proceedings of The IEEE Conference On Computer Vision and Pattern Recognition, Boston, Massachusetts.

He K., Zhang X., and R S., (2016). "Deep residual learning for image recognition," in Proceedings of The IEEE Conference On Computer Vision and Pattern Recognition, Las Vegas, NV, USA.

COVID-CT dataset. [online], available at:




How to Cite

Raheem, F., & Manaf K. Hussein. (2023). COVID-19 detection using machine learning and fusion-based deep learning models. Wasit Journal of Engineering Sciences, 11(2), 12-23.